{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import gym\n",
    "import itertools\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import sys\n",
    "import sklearn.pipeline\n",
    "import sklearn.preprocessing\n",
    "\n",
    "if \"../\" not in sys.path:\n",
    "  sys.path.append(\"../\") \n",
    "\n",
    "from lib import plotting\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.kernel_approximation import RBFSampler\n",
    "\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2016-11-06 15:54:37,301] Making new env: MountainCar-v0\n"
     ]
    }
   ],
   "source": [
    "env = gym.envs.make(\"MountainCar-v0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FeatureUnion(n_jobs=1,\n",
       "       transformer_list=[('rbf1', RBFSampler(gamma=5.0, n_components=100, random_state=None)), ('rbf2', RBFSampler(gamma=2.0, n_components=100, random_state=None)), ('rbf3', RBFSampler(gamma=1.0, n_components=100, random_state=None)), ('rbf4', RBFSampler(gamma=0.5, n_components=100, random_state=None))],\n",
       "       transformer_weights=None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Preprocessing: Normalize to zero mean and unit variance\n",
    "# We use a few samples from the observation space to do this\n",
    "observation_examples = np.array([env.observation_space.sample() for x in range(10000)])\n",
    "scaler = sklearn.preprocessing.StandardScaler()\n",
    "scaler.fit(observation_examples)\n",
    "\n",
    "# Used to convert a state to a featurizes represenation.\n",
    "# We use RBF kernels with different variances to cover different parts of the space\n",
    "featurizer = sklearn.pipeline.FeatureUnion([\n",
    "        (\"rbf1\", RBFSampler(gamma=5.0, n_components=100)),\n",
    "        (\"rbf2\", RBFSampler(gamma=2.0, n_components=100)),\n",
    "        (\"rbf3\", RBFSampler(gamma=1.0, n_components=100)),\n",
    "        (\"rbf4\", RBFSampler(gamma=0.5, n_components=100))\n",
    "        ])\n",
    "featurizer.fit(scaler.transform(observation_examples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Estimator():\n",
    "    \"\"\"\n",
    "    Value Function approximator. \n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        # We create a separate model for each action in the environment's\n",
    "        # action space. Alternatively we could somehow encode the action\n",
    "        # into the features, but this way it's easier to code up.\n",
    "        self.models = []\n",
    "        for _ in range(env.action_space.n):\n",
    "            model = SGDRegressor(learning_rate=\"constant\")\n",
    "            # We need to call partial_fit once to initialize the model\n",
    "            # or we get a NotFittedError when trying to make a prediction\n",
    "            # This is quite hacky.\n",
    "            model.partial_fit([self.featurize_state(env.reset())], [0])\n",
    "            self.models.append(model)\n",
    "    \n",
    "    def featurize_state(self, state):\n",
    "        \"\"\"\n",
    "        Returns the featurized representation for a state.\n",
    "        \"\"\"\n",
    "        scaled = scaler.transform([state])\n",
    "        featurized = featurizer.transform(scaled)\n",
    "        return featurized[0]\n",
    "    \n",
    "    def predict(self, s, a=None):\n",
    "        \"\"\"\n",
    "        Makes value function predictions.\n",
    "        \n",
    "        Args:\n",
    "            s: state to make a prediction for\n",
    "            a: (Optional) action to make a prediction for\n",
    "            \n",
    "        Returns\n",
    "            If an action a is given this returns a single number as the prediction.\n",
    "            If no action is given this returns a vector or predictions for all actions\n",
    "            in the environment where pred[i] is the prediction for action i.\n",
    "            \n",
    "        \"\"\"\n",
    "        features = self.featurize_state(s)\n",
    "        if not a:\n",
    "            return np.array([m.predict([features])[0] for m in self.models])\n",
    "        else:\n",
    "            return self.models[a].predict([features])[0]\n",
    "    \n",
    "    def update(self, s, a, y):\n",
    "        \"\"\"\n",
    "        Updates the estimator parameters for a given state and action towards\n",
    "        the target y.\n",
    "        \"\"\"\n",
    "        features = self.featurize_state(s)\n",
    "        self.models[a].partial_fit([features], [y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_epsilon_greedy_policy(estimator, epsilon, nA):\n",
    "    \"\"\"\n",
    "    Creates an epsilon-greedy policy based on a given Q-function approximator and epsilon.\n",
    "    \n",
    "    Args:\n",
    "        estimator: An estimator that returns q values for a given state\n",
    "        epsilon: The probability to select a random action . float between 0 and 1.\n",
    "        nA: Number of actions in the environment.\n",
    "    \n",
    "    Returns:\n",
    "        A function that takes the observation as an argument and returns\n",
    "        the probabilities for each action in the form of a numpy array of length nA.\n",
    "    \n",
    "    \"\"\"\n",
    "    def policy_fn(observation):\n",
    "        A = np.ones(nA, dtype=float) * epsilon / nA\n",
    "        q_values = estimator.predict(observation)\n",
    "        best_action = np.argmax(q_values)\n",
    "        A[best_action] += (1.0 - epsilon)\n",
    "        return A\n",
    "    return policy_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def q_learning(env, estimator, num_episodes, discount_factor=1.0, epsilon=0.1, epsilon_decay=1.0):\n",
    "    \"\"\"\n",
    "    Q-Learning algorithm for fff-policy TD control using Function Approximation.\n",
    "    Finds the optimal greedy policy while following an epsilon-greedy policy.\n",
    "    \n",
    "    Args:\n",
    "        env: OpenAI environment.\n",
    "        estimator: Action-Value function estimator\n",
    "        num_episodes: Number of episodes to run for.\n",
    "        discount_factor: Gamma discount factor.\n",
    "        epsilon: Chance the sample a random action. Float betwen 0 and 1.\n",
    "        epsilon_decay: Each episode, epsilon is decayed by this factor\n",
    "    \n",
    "    Returns:\n",
    "        An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n",
    "    \"\"\"\n",
    "\n",
    "    # Keeps track of useful statistics\n",
    "    stats = plotting.EpisodeStats(\n",
    "        episode_lengths=np.zeros(num_episodes),\n",
    "        episode_rewards=np.zeros(num_episodes))    \n",
    "    \n",
    "    for i_episode in range(num_episodes):\n",
    "        \n",
    "        # The policy we're following\n",
    "        policy = make_epsilon_greedy_policy(\n",
    "            estimator, epsilon * epsilon_decay**i_episode, env.action_space.n)\n",
    "        \n",
    "        # Print out which episode we're on, useful for debugging.\n",
    "        # Also print reward for last episode\n",
    "        last_reward = stats.episode_rewards[i_episode - 1]\n",
    "        sys.stdout.flush()\n",
    "        \n",
    "        # Reset the environment and pick the first action\n",
    "        state = env.reset()\n",
    "        \n",
    "        # Only used for SARSA, not Q-Learning\n",
    "        next_action = None\n",
    "        \n",
    "        # One step in the environment\n",
    "        for t in itertools.count():\n",
    "                        \n",
    "            # Choose an action to take\n",
    "            # If we're using SARSA we already decided in the previous step\n",
    "            if next_action is None:\n",
    "                action_probs = policy(state)\n",
    "                action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\n",
    "            else:\n",
    "                action = next_action\n",
    "            \n",
    "            # Take a step\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "    \n",
    "            # Update statistics\n",
    "            stats.episode_rewards[i_episode] += reward\n",
    "            stats.episode_lengths[i_episode] = t\n",
    "            \n",
    "            # TD Update\n",
    "            q_values_next = estimator.predict(next_state)\n",
    "            \n",
    "            # Use this code for Q-Learning\n",
    "            # Q-Value TD Target\n",
    "            td_target = reward + discount_factor * np.max(q_values_next)\n",
    "            \n",
    "            # Use this code for SARSA TD Target for on policy-training:\n",
    "            # next_action_probs = policy(next_state)\n",
    "            # next_action = np.random.choice(np.arange(len(next_action_probs)), p=next_action_probs)             \n",
    "            # td_target = reward + discount_factor * q_values_next[next_action]\n",
    "            \n",
    "            # Update the function approximator using our target\n",
    "            estimator.update(state, action, td_target)\n",
    "            \n",
    "            print(\"\\rStep {} @ Episode {}/{} ({})\".format(t, i_episode + 1, num_episodes, last_reward), end=\"\")\n",
    "                \n",
    "            if done:\n",
    "                break\n",
    "                \n",
    "            state = next_state\n",
    "    \n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimator = Estimator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 110 @ Episode 100/100 (-163.0)"
     ]
    }
   ],
   "source": [
    "# Note: For the Mountain Car we don't actually need an epsilon > 0.0\n",
    "# because our initial estimate for all states is too \"optimistic\" which leads\n",
    "# to the exploration of all states.\n",
    "stats = q_learning(env, estimator, 100, epsilon=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LglmjrBVtyViiFFgyT13fDmu6XwJBfUJTMt3d3R13d3c6depU5b7S0tK4desW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\nQNBu/H/KEP1vONfcjwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1084514e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mx/RGrYW84cOHM3z4cMB9q5533nmHO+64g1mzZvHJJ5/Qo0cP1qxZQ0ZGBuCewHPNmjWk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\nof8HO9HRQeCFOXgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108451eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x4yZ/Hv/IDFUsk7p2P0dz4mPFuPpYLsOHD0fr1q0faRv1sVweFZeJcVwuxpWX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\nCsZij4iIiEjBWOwRERERKdj/AUim4jw9NBFjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107bb5e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AgH75EvRpb0NVrAz17iyoEiWNjkT3iQWPiIiIIInxSJs7FqrR41DPd4FSyuhI9ABY8IiI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\nAEDMEPAAAABihoAHAAAQM98AX7OOAecyhTIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107e9c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x108451eb8>,\n",
       " <matplotlib.figure.Figure at 0x107bb5e80>,\n",
       " <matplotlib.figure.Figure at 0x107e9c7f0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotting.plot_cost_to_go_mountain_car(env, estimator)\n",
    "plotting.plot_episode_stats(stats, smoothing_window=25)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
